Cluster Identification

workflowr

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Last updated: 2024-01-11

Checks: 7 0

Knit directory: files/

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Repository version: f7ee227

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    Ignored:    .Rhistory
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Untracked files:
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    Untracked:  data/QC_dataFinal.rds
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Unstaged changes:
    Modified:   .gitignore
    Modified:   workflowHelper.R

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/02_01_Clustering_scRNA.Rmd) and HTML (docs/02_01_Clustering_scRNA.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	9044a86	Andreas Chiocchetti	2024-01-11	Build site.
html	f206c28	Andreas Chiocchetti	2024-01-11	wflow_publish(c("./analysis/.Rmd", "./code/", "./docs/*"))
Rmd	bca1a73	Andreas Chiocchetti	2024-01-09	intermediate stage bulk seq

---

Clustering Analysis

Clustering after batch correction

Centering and scaling data matrix

PC_ 1 
Positive:  STMN2, NSG2, NEUROD6, INA, NEUROD2, BHLHE22, CXADR, SLA, GAP43, NELL2 
       TTC9B, GRIA2, MYT1L, LRRC7, MLLT3, LBH, UCHL1, DLX6-AS1, STMN4, OCIAD2 
       CNR1, SYT4, ENC1, MEF2C, SNAP25, RUNX1T1, ERBB4, SNCB, ZBTB18, GRIA1 
Negative:  SLC1A3, VIM, HMGB2, ZFP36L1, NUSAP1, TOP2A, DBI, PTN, B2M, MKI67 
       PTTG1, CLU, CD99, PTPRZ1, CDK1, SPARC, PON2, HOPX, UBE2C, METRN 
       TTYH1, TPX2, PBK, CENPF, ANXA5, MT2A, BCAN, SOX9, PEA15, HSPB1 
PC_ 2 
Positive:  MKI67, UBE2C, TOP2A, NUSAP1, TPX2, CENPF, KIF2C, DLGAP5, ASPM, BIRC5 
       KNL1, PIMREG, CDC20, CDCA8, NUF2, PBK, CDK1, SGO1, PTTG1, HMGB2 
       KIF11, PLK1, CCNA2, CKAP2L, KIFC1, GTSE1, CCNB1, NDC80, CENPE, MAD2L1 
Negative:  CLU, ATP1B2, PTN, AQP4, HOPX, PON2, TFPI, ANOS1, ATP1A2, TTYH1 
       SPARC, BCAN, SLC1A3, APOE, PSAT1, VIM, PEA15, FAM107A, PTPRZ1, HES1 
       TIMP3, IL33, LRRC3B, CSPG5, S1PR1, SLCO1C1, SCD, VCAM1, TNC, IQGAP2 
PC_ 3 
Positive:  NEUROD6, NELL2, NEUROD2, MEF2C, BHLHE22, GAP43, ARPP21, SATB2, SERPINI1, SYT4 
       ZBTB18, SLA, FAM49A, GPR22, NEFM, CAMK2B, GPR85, SNCB, CSRP2, NSG2 
       CXADR, LINGO1, SATB2-AS1, PLXNA4, DAB1, OCIAD2, GPRIN3, NRN1, PCLO, FAM162A 
Negative:  DLX6-AS1, PLS3, SCGN, DLX2, DLX5, DLX1, SOX2-OT, GAD2, CALB2, SP9 
       PDZRN3, ERBB4, RND3, ID4, SMOC1, C1orf61, NNAT, GAD1, WLS, SOX9 
       NRIP3, TOX3, ST18, HMGN2, AMBN, NRXN3, CDCA7, DBI, BCAN, PCDH9 
PC_ 4 
Positive:  ADM, VEGFA, DDIT4, BNIP3, IGFBP2, P4HA1, PLOD2, EGLN3, SLC16A3, SLC2A1 
       FAM162A, ENO1, STC2, AKAP12, PGK1, IGFBP5, GAPDH, PDK1, SLC16A1, TPI1 
       AK4, CEBPB, PKM, MIR210HG, HERPUD1, SHMT2, EMX2, HK2, BHLHE40, CDKN1A 
Negative:  NTRK2, AQP4, SPARCL1, APOE, GJA1, CST3, SPON1, MEF2C, PMP2, ANOS1 
       TFPI, S100B, BCAN, CHL1, STMN2, DCLK1, CSPG5, NKAIN4, BBOX1, VCAM1 
       LINC01896, AGT, CALM1, SATB2, ANGPT1, RANBP3L, SERPINI1, WLS, NELL2, GFAP 
PC_ 5 
Positive:  ENC1, TMEM158, BHLHE22, NEUROD2, EZR, SLA, CSRP2, MLLT3, CNR1, PHLDA1 
       NEUROD6, CNTNAP2, LHX2, ADRA2A, NKAIN3, ZBTB18, HES6, EOMES, EPHA3, CLMP 
       NHLH1, FABP7, RASGRP1, NEUROG2, SFRP1, CHRDL1, HS3ST1, PENK, GAP43, GNG5 
Negative:  DLX6-AS1, PLS3, SCGN, DLX2, SOX2-OT, DLX1, DLX5, PLOD2, STC2, PDZRN3 
       GPRIN3, GPR22, CALB2, ADM, DDIT4, CALY, BNIP3, SERPINI1, GAD2, ERBB4 
       VEGFA, H1F0, FAM162A, SP9, PCDH9, IGFBP5, CNTN1, CELF4, PDK1, P4HA1 

Computing nearest neighbor graph

Computing SNN

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9383
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9007
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8746
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8516
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8312
Number of communities: 16
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8164
Number of communities: 19
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8028
Number of communities: 20
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7900
Number of communities: 21
Elapsed time: 0 seconds

UMAP Clustering after batch correction at different resolutions

Past versions of unnamed-chunk-1-1.png

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

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Check stability of clusters

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

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Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8746
Number of communities: 14
Elapsed time: 0 seconds

optimize UMAP

<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>

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Past versions of unnamed-chunk-4-1.png

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

Saving 12 x 12 in image

Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session

17:58:06 UMAP embedding parameters a = 0.1496 b = 0.8684
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
17:58:06 Read 9107 rows and found 40 numeric columns
17:58:06 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
17:58:06 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:58:07 Writing NN index file to temp file /tmp/RtmpMYFmma/file6fe9424acf8d
17:58:07 Searching Annoy index using 1 thread, search_k = 3000
17:58:09 Annoy recall = 100%
17:58:10 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
17:58:12 Initializing from normalized Laplacian + noise (using RSpectra)
17:58:12 Commencing optimization for 500 epochs, with 415428 positive edges
17:58:22 Optimization finished

final UMAP clustering

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

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Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

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Calculate cell cycle scoring

Warning: The following features are not present in the object: FEN1, MLF1IP,
RAD51, not searching for symbol synonyms

Warning: The following features are not present in the object: FAM64A, HN1, not
searching for symbol synonyms

Past versions of "Cell cycle scoring-1.png"

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

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Identification of clusters

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

          0mM       5mM
0mM 1.0000000 0.6908695
5mM 0.6908695 1.0000000

Using type as id variables

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

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Feature plots UMAP

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

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Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

Feature plots PCA

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

Saving 8 x 8 in image

Markers identification and visualization

Calculating cluster 0

For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the presto package
--------------------------------------------
install.packages('devtools')
devtools::install_github('immunogenomics/presto')
--------------------------------------------
After installation of presto, Seurat will automatically use the more 
efficient implementation (no further action necessary).
This message will be shown once per session

Calculating cluster 1

Calculating cluster 2

Calculating cluster 3

Calculating cluster 4

Calculating cluster 5

Calculating cluster 6

Calculating cluster 7

Calculating cluster 8

Calculating cluster 9

Calculating cluster 10

Calculating cluster 11

Calculating cluster 12

Calculating cluster 13

Warning in DoHeatmap(seurat_integrated, features = top10$gene, slot =
"scale.data"): The following features were omitted as they were not found in
the scale.data slot for the RNA assay: CYP4F26P, PPARG, IGLV1-51, TRHDE-AS1,
AL133375.1, DCHS2, AC026471.3, CNOT6LP1, AC103810.2, ARMC3, LINC01497,
AC084125.2, AC120036.1, POU4F1, NAMPTP1, AC010320.1, LHX5-AS1, AC091182.1,
NKX2-5, HOXD9, MYOD1, LHX5, ULBP1, AL157400.2, UPK1A-AS1, AC099850.3, FAM72C,
FBLN2, HSPB3, MYO3B, IMPG2, VIPR2, LINC00689, TESK2, AP001972.3, DENND1C,
CRYBG2, MCHR1, RELN, EPS8L2, ASCL2, CABP7, FXYD3, PI16, KCNJ16, GJB2, FIBIN,
MME, OXTR, DMRTA1, AC087632.1, KANK2, CDC45, AC099754.1, AC092112.1, IL12A,
CASP1, FMO1, TMEM244, GPR61, AL139275.1, AC010931.2, AKAIN1, CEMIP, LRTM2,
UNC5A, LAMB3, PTGFR, SVEP1, MYOT, CACNA2D3, PTPRR, RGN, THRB

Past versions of "Identifying markers-1.png"

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

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Visualizing the expression of marker genes with respect to different cell types

Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
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Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds

Using Group.1 as id variables

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
f206c28	Andreas Chiocchetti	2024-01-11

Saving 6 x 9 in image

GO terms of clusters

Detected custom background input, domain scope is set to 'custom'

[1] "result" "meta"  

Session information

R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] clustree_0.5.1              ggraph_2.1.0               
 [3] CATALYST_1.24.0             reshape2_1.4.4             
 [5] pals_1.8                    gprofiler2_0.2.2           
 [7] viridis_0.6.4               viridisLite_0.4.2          
 [9] cowplot_1.1.2               randomcoloR_1.1.0.1        
[11] RCurl_1.98-1.13             RColorBrewer_1.1-3         
[13] data.table_1.14.10          lubridate_1.9.3            
[15] forcats_1.0.0               stringr_1.5.1              
[17] dplyr_1.1.4                 purrr_1.0.2                
[19] readr_2.1.4                 tidyr_1.3.0                
[21] tibble_3.2.1                tidyverse_2.0.0            
[23] scater_1.28.0               scuttle_1.10.3             
[25] Seurat_5.0.1                SeuratObject_5.0.1         
[27] sp_2.1-2                    SingleCellExperiment_1.24.0
[29] ggpubr_0.6.0                ggplot2_3.4.4              
[31] SingleR_2.2.0               SummarizedExperiment_1.32.0
[33] Biobase_2.62.0              GenomicRanges_1.54.1       
[35] GenomeInfoDb_1.38.1         IRanges_2.36.0             
[37] S4Vectors_0.40.2            BiocGenerics_0.48.1        
[39] MatrixGenerics_1.14.0       matrixStats_1.1.0          
[41] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.3                    spatstat.sparse_3.0-3      
  [3] bitops_1.0-7                httr_1.4.7                 
  [5] doParallel_1.0.17           tools_4.3.1                
  [7] sctransform_0.4.1           backports_1.4.1            
  [9] DT_0.31                     utf8_1.2.4                 
 [11] R6_2.5.1                    lazyeval_0.2.2             
 [13] uwot_0.1.16                 GetoptLong_1.0.5           
 [15] withr_2.5.2                 gridExtra_2.3              
 [17] progressr_0.14.0            textshaping_0.3.7          
 [19] cli_3.6.2                   spatstat.explore_3.2-5     
 [21] fastDummies_1.7.3           sandwich_3.1-0             
 [23] labeling_0.4.3              sass_0.4.8                 
 [25] nnls_1.5                    mvtnorm_1.2-4              
 [27] spatstat.data_3.0-3         ggridges_0.5.5             
 [29] pbapply_1.7-2               systemfonts_1.0.5          
 [31] svglite_2.1.3               colorRamps_2.3.1           
 [33] dichromat_2.0-0.1           parallelly_1.36.0          
 [35] plotrix_3.8-4               limma_3.56.2               
 [37] maps_3.4.2                  flowCore_2.12.2            
 [39] rstudioapi_0.15.0           shape_1.4.6                
 [41] generics_0.1.3              crosstalk_1.2.1            
 [43] gtools_3.9.5                ica_1.0-3                  
 [45] spatstat.random_3.2-2       zip_2.3.0                  
 [47] car_3.1-2                   RProtoBufLib_2.12.1        
 [49] Matrix_1.6-4                ggbeeswarm_0.7.2           
 [51] fansi_1.0.6                 abind_1.4-5                
 [53] lifecycle_1.0.4             multcomp_1.4-25            
 [55] whisker_0.4.1               yaml_2.3.8                 
 [57] carData_3.0-5               SparseArray_1.2.2          
 [59] Rtsne_0.17                  grid_4.3.1                 
 [61] promises_1.2.1              crayon_1.5.2               
 [63] miniUI_0.1.1.1              lattice_0.22-5             
 [65] beachmat_2.16.0             mapproj_1.2.11             
 [67] pillar_1.9.0                knitr_1.45                 
 [69] ComplexHeatmap_2.16.0       rjson_0.2.21               
 [71] future.apply_1.11.1         codetools_0.2-19           
 [73] leiden_0.4.3.1              glue_1.6.2                 
 [75] getPass_0.2-4               V8_4.4.1                   
 [77] vctrs_0.6.5                 png_0.1-8                  
 [79] spam_2.10-0                 gtable_0.3.4               
 [81] cachem_1.0.8                xfun_0.41                  
 [83] S4Arrays_1.2.0              mime_0.12                  
 [85] tidygraph_1.3.0             ConsensusClusterPlus_1.64.0
 [87] survival_3.5-7              pheatmap_1.0.12            
 [89] iterators_1.0.14            cytolib_2.12.1             
 [91] TH.data_1.1-2               ellipsis_0.3.2             
 [93] fitdistrplus_1.1-11         ROCR_1.0-11                
 [95] nlme_3.1-164                RcppAnnoy_0.0.21           
 [97] rprojroot_2.0.4             bslib_0.6.1                
 [99] irlba_2.3.5.1               vipor_0.4.7                
[101] KernSmooth_2.23-22          colorspace_2.1-0           
[103] ggrastr_1.0.2               tidyselect_1.2.0           
[105] processx_3.8.3              compiler_4.3.1             
[107] curl_5.2.0                  git2r_0.33.0               
[109] BiocNeighbors_1.18.0        DelayedArray_0.28.0        
[111] plotly_4.10.3               checkmate_2.3.1            
[113] scales_1.3.0                lmtest_0.9-40              
[115] callr_3.7.3                 digest_0.6.33              
[117] goftest_1.2-3               spatstat.utils_3.0-4       
[119] rmarkdown_2.25              XVector_0.42.0             
[121] htmltools_0.5.7             pkgconfig_2.0.3            
[123] openxlsx2_1.2               sparseMatrixStats_1.12.2   
[125] highr_0.10                  fastmap_1.1.1              
[127] rlang_1.1.2                 GlobalOptions_0.1.2        
[129] htmlwidgets_1.6.4           shiny_1.8.0                
[131] DelayedMatrixStats_1.22.6   farver_2.1.1               
[133] jquerylib_0.1.4             zoo_1.8-12                 
[135] jsonlite_1.8.8              BiocParallel_1.34.2        
[137] BiocSingular_1.16.0         magrittr_2.0.3             
[139] GenomeInfoDbData_1.2.11     dotCall64_1.1-1            
[141] patchwork_1.2.0             munsell_0.5.0              
[143] Rcpp_1.0.11                 ggnewscale_0.4.9           
[145] reticulate_1.34.0           stringi_1.8.3              
[147] zlibbioc_1.48.0             MASS_7.3-60                
[149] plyr_1.8.9                  parallel_4.3.1             
[151] listenv_0.9.0               ggrepel_0.9.4              
[153] deldir_2.0-2                graphlayouts_1.0.2         
[155] splines_4.3.1               tensor_1.5                 
[157] circlize_0.4.15             hms_1.1.3                  
[159] ps_1.7.5                    igraph_1.6.0               
[161] spatstat.geom_3.2-7         ggsignif_0.6.4             
[163] RcppHNSW_0.5.0              ScaledMatrix_1.8.1         
[165] XML_3.99-0.16               drc_3.0-1                  
[167] evaluate_0.23               tweenr_2.0.2               
[169] tzdb_0.4.0                  foreach_1.5.2              
[171] httpuv_1.6.13               RANN_2.6.1                 
[173] polyclip_1.10-6             clue_0.3-65                
[175] future_1.33.1               scattermore_1.2            
[177] ggforce_0.4.1               rsvd_1.0.5                 
[179] broom_1.0.5                 xtable_1.8-4               
[181] RSpectra_0.16-1             rstatix_0.7.2              
[183] later_1.3.2                 ragg_1.2.7                 
[185] FlowSOM_2.8.0               beeswarm_0.4.0             
[187] cluster_2.1.6               timechange_0.2.0           
[189] globals_0.16.2